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Home / programming language / Online Matlab Projects / Automatic Detection and Measurement of Structuresin Fetal Head Ultrasound Volumes Using SequentialEstimation and Integrated Detection Network (IDN)
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Automatic Detection and Measurement of Structuresin Fetal Head Ultrasound Volumes Using SequentialEstimation and Integrated Detection Network (IDN)

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SKU: PROJ3016 Categories: 2015 Projects, Digital Image Processing Projects, Final Year Projects, Online Matlab Projects Tags: academic projects, Andoid project 2015-2016, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project 2015-2016, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java project list, java projects 2013-2014, java projects 2015-2016, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project 2015-2016, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, php projects 2015-2016, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, vlsi prjects 2015-2016, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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Automatic Detection and Measurement of Structuresin Fetal Head Ultrasound Volumes Using SequentialEstimation and Integrated Detection Network (IDN)

Abstract— Automatic Detection and Measurement of Structuresin Fetal Head Ultrasound Volumes Using SequentialEstimation and Integrated Detection Network. Routine ultrasound exam in the second and third trimesters of pregnancy involves manually measuring fetal head and brain structures in 2-D scans. The procedure requires a sonographer to find the standardized visualization planes with a probe and manually place measurement calipers on the structures of interest. The process is tedious, time consuming, and introduces user variability into the measurements. This paper proposes an automatic fetal head and brain (AFHB) system for automatically measuring anatomical structures from 3-D ultrasound volumes. The system searches the 3-D volume in a hierarchy of resolutions and by focusing on regions that are likely to be the measured anatomy. The output is a standardized visualization of the plane with correct orientation and centering as well as the biometric measurement of the anatomy. The system is based on a novel framework for detecting multiple structures in 3-D volumes. Since a joint model is difficult to obtain in most practical situations, the structures are detected in a sequence, one-by-one. The detection relies on Sequential Estimation techniques, frequently applied to visual tracking. The interdependence of structure poses and strong prior information embedded in our domain yields faster and more accurate results than detecting the objects individually. The posterior distribution of the structure pose is approximated at each step by sequential Monte Carlo. The samples are propagated within the sequence across multiple structures and hierarchical levels. The probabilistic model helps solve many challenges present in the ultrasound images of the fetus such as speckle noise, signal drop-out, shadows caused by bones, and appearance variations caused by the differences in the fetus gestational age. This is possible by discriminative learning on an extensive database of scans comprising more than two thousand volumes and more than thirteen thousand annotations. The average difference between ground truth and automatic measurements is below 2 mm with a running time of 6.9 s (GPU) or 14.7 s (CPU). The accuracy of the AFHB system is within inter-user variability and the running time is fast, which meets the requirements for clinical use. (IDN) < Final Year Projects >

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